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Record W2068545368 · doi:10.1002/jbio.201100026

Quantitative remineralization evolution kinetics of artificially demineralized human enamel using photothermal radiometry and modulated luminescence

2011· article· en· W2068545368 on OpenAlexaff
Adam Hellen, Andreas Mandelis, Yoav Finer, Bennett T. Amaechi

Bibliographic record

VenueJournal of Biophotonics · 2011
Typearticle
Languageen
FieldEngineering
TopicThermography and Photoacoustic Techniques
Canadian institutionsXanadu Quantum Technologies (Canada)University of Toronto
Fundersnot available
KeywordsRemineralisationDemineralizationEnamel paintRadiometryLuminescenceChemistryTooth RemineralizationFluorideMaterials scienceAnalytical Chemistry (journal)MineralogyOpticsOptoelectronicsComposite materialEnvironmental chemistryInorganic chemistry

Abstract

fetched live from OpenAlex

Human molars were subjected to demineralization in acid gel followed by incubation in remineralization solutions without or with fluoride (1 or 1000 ppm). Photothermal radiometry (PTR) and modulated luminescence (LUM) frequency scans were performed prior to and during de/remineralization treatments. Transverse Micro-Radiography (TMR) analysis followed at treatment conclusion to determine mineral loss and lesion depth. The remineralization process illustrated a complex interplay between surface and subsurface mineral deposition, confining the thermal-wave centroid toward the dominating layer. Experimental amplitudes and phases were fitted to a coupled diffuse-photon-density-wave and thermal-wave theoretical model used to quantitatively evaluate evolving changes in thermal and optical properties of de/remineralized enamel lesions. Additional information obtained from the LUM data corroborated the remineralization kinetics affecting the PTR signals. The results pointed to enhanced effectiveness of subsurface lesion remineralization in the presence of fluoride.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.118
Threshold uncertainty score0.586

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.038
GPT teacher head0.248
Teacher spread0.210 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations13
Published2011
Admission routes1
Has abstractyes

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